Correlation coefficients of vibration signals and machine learning algorithm for structural damage assessment in beams under moving load

This paper presents a novel method of assessing structural damage in beams exposed to moving loads via acceleration signals through experimental studies. In this study, beams are supported on both ends, and their dynamic response to moving loads is assessed. The raw signal has been improved using a random decrement technique. Take measurements from different locations and calculate correlation coefficients between them, then use these as features to evaluate the structure. In order to create a reliable and potential framework for predicting damage efficiently, these features are used as input variables to the machine learning model. The proposed methodology exhibits promising results in accurately discerning and predicting damage in beam structure. It demonstrates a high level of precision to subtle changes in structural integrity when trained by machine learning on the statistical feature extracted from acceleration signals. As a result of this research, methods for detecting structural damage can be made more reliable and efficient by employing machine learning techniques. Additionally, structures operating in dynamic environments can benefit significantly from the proposed methodology

An interface-based microscopic model for the failure analysis of masonry structures reinforced with timber retrofit solutions

This paper presents a refined Finite Element (FE) modeling strategy for analyzing the failure behavior of regular masonry structures reinforced with timber-based retrofit solutions. The proposed model schematizes the masonry as brick units, modeled using two-dimensional linear elastic plane stress elements, mutually joined through zero-thickness cohesive interface elements. These interface elements serve to reproduce the nonlinear behavior of masonry because of the occurrence of failure mechanisms of the mortar joints. Reinforced timber frame elements are modeled using truss elements that exhibit elastic brittle fracture behavior. The interaction between the masonry sub-structure and the reinforced timber frame system is accounted for using special constraint conditions that simulate the mechanical behavior of anchorage connections. The reliability of the proposed model in reproducing the failure behavior of masonry is assessed through comparisons with experimental and numerical data available in the literature. Additionally, the efficacy of the retrofit technique based on timber frame structures is investigated in detail through pushover analyses on a two-story masonry wall representative of real-life masonry buildings. The results indicate that the proposed retrofitting strategy is an effective and eco-friendly retrofit solution to enhance the in-plane bearing capacity of masonry structures subjected to horizontal forces

Uniaxial fatigue study of a natural-based bio-composite material reinforced with fique natural fibers

This research addresses environmental concerns by exploring environmentally friendly composite materials as substitutes for non-biodegradable synthetic fibers. The study proposes the development of polymer matrix composites reinforced with natural fique fibers, sourced from a plant cultivated in Colombia. A BioPoxy 36 polymer matrix with a high carbon content was used and reinforced with fique fabric using the vacuum-assisted lamination method. To improve the adhesion between the fibers and the matrix, an alkaline chemical treatment was applied to the fiber using 2% sodium hydroxide by weight. Mechanical properties were assessed through ASTM D3039 tensile and ASTM D3479 fatigue tests. A fractographic analysis was also conducted to identify the different modes of failure present. In terms of material degradation, distinct stages were observed, characterized by stiffness loss and loss factor indicators. The Coffin-Manson model was used to obtain the strain life curve for R = 0.1, using these factors as criteria. The static properties of the composite reinforced with fique fibers indicate an increase of 45% in ultimate strength, 145% in strain, and 27% in Young's modulus compared to the unreinforced matrix. In terms of dynamic properties, the elastic modulus showed a maximum variation of up to 7.88%. Electron microscopy reveals the failure mechanism, a distinct separation between the matrix and the fiber can be observed as a result of mechanical stress. The analysis reveals the brittle fracture of the hard fique fiber and some matrix separation, as well as possible fractured bubbles that may have occurred during the manufacturing process

Civil Structural Health Monitoring and Machine Learning: A Comprehensive Review

In the past five years, the implementation of machine learning (ML) techniques has surged in civil engineering applications, particularly for optimizing and predicting solutions to various challenges.  More robust prediction models may be produced by combining test data collected in the laboratory or field with ML. These models may be used to estimate the compressive strength of masonry or repair mortars, probable damage scenarios in buildings, concrete models, beams, and columns for determining the mechanical characteristics of materials, damage detection in civil structures, and so on.  This comprehensive review aims to clarify the array of ML-based methods employed in civil engineering, specifically focusing on their efficacy in strengthening energy efficiency and cost-effectiveness. In combination with ML, the review explores corresponding soft computing methodologies such as fuzzy logic (FL) and design of experiments (DOE). A variety of case examples that highlight the versatility of these approaches, particularly in applications linked to structural reinforcement, enhance the story. The review navigates difficulties associated with the integration of soft computing in civil engineering and expands its scope to include emerging research directions. This synthesis of advanced artificial intelligence (AI) serves as a guide, providing new researchers with knowledge about a developing field. These methods could revolutionize the current situation by providing creative answers to complex problems that arise in civil structural applications

Mechanical behavior of fiber-glass plastic with hole pattern using digital image correlation and acoustic emission methods

In this paper, tensile tests of specimens with a pattern of holes made of fiber-glass plastic based on combined epoxy and phenol-formaldehyde resins are carried out in order to study the processes of damage accumulation and tension fracture. The Vic-3D video system is used to evaluate damage development and inhomogeneity of strain localization during loading. Continuous recording of acoustic emission signals is carried out during the tests, resulting in obtaining data on fracture mechanisms in the material. Ranges of peak frequencies are identified. Surface analysis of specimens was carried out using a microscope. A significant reduction in strength occurs due to the presence of a circular hole in the material, although additional holes do not exacerbate this effect. Fracture patterns of specimens with a hole pattern have been analyzed, and different "paths" of fracture have been observed. The comparison of strain fields obtained on the basis of application of three-dimensional digital optical system with the configuration of strain fields constructed as a result of numerical modeling by the finite element method has been carried out. It is found that the strain fields for different open hole patterns are quantitatively and qualitatively similar and identical

Fatigue behavior of pultruded fiberglass tubes under tension, compression and torsion

This work is devoted to an experimental investigation of fatigue behavior of pultruded fiberglass tubes under uniaxial tension, compression and torsion. Static tests were carried out; a presence of postcritical deformation stage during torsion is noted. Regularities of inhomogeneous strain fields evolution are analyzed using digital image correlation method. Fatigue curves are built for four cyclic loading modes: tension-tension, compression-compression, tension-compression and torsion. An analysis of specimens' fractures is carried out, typical damaging mechanisms are revealed. Residual dynamic stiffness data is obtained and studied using a previously proposed fitting model. Results demonstrate model's high descriptive capability and its flexibility to describe two-staged and three-staged stiffness degradation curves. An influence of loading mode on a shape of these curves is found out. Model parameters' dependence on maximum stress value during the loading cycle is studied using the Pearson's correlation coefficient. The necessity of multiaxial fatigue behavior investigation of pultruded fiberglass tubes is concluded

Introduction and application of a drive-by damage detection methodology for bridges using variational mode decomposition

In this research, the variational mode decomposition (VMD) method is used for the drive-by health monitoring of bridges. Firstly, the problem of a half-trailer tractor moving over a bridge is formulated. Next, a Finite Element (FE) code is developed and verified against modal analysis results where complete agreement is found. The vehicle's output signals are decomposed through VMD and then analyzed to identify and precisely locate damage in the bridge structure. The range of applicability of this technique is examined from different perspectives by including various road classes, damage severity and location, and noise. The results prove the robustness and reliability of using VMD for drive-by damage detection. The method outcomes indicate that through the VMD method, cracks with a depth of 10% to 20% of the beam height can be detected even in the case of a rough road profile. A comparison of the results of the VMD and the well-known empirical mode decomposition (EMD) method has also been conducted. This comparison reveals that by implementing the VMD, precise damage locations can be determined, whereas the EMD fails to detect any damage under the conditions considered in this study. The effects of noise and moving vehicle speed are also investigated in the research, and it is found that processing the output signals using VMD can yield reliable estimates of the damage location(s)

About Measuring the Stress Intensity Factor of Cracks in Curved, Brittle Shells

Most techniques of measuring the stress intensity factor (SIF) in the cracking process assume a crack in a planar medium. Currently, there is no effective approach for curved brittle shells, particularly for non-developable cases, i.e., shapes with non-vanishing Gaussian curvature. This paper introduces a novel approach to obtaining material properties related to fracture by experimentally observing weakly curved surfaces. Based on the DIC record of the displacement field around the crack tip, the truncated Williams expansion is fitted to the data adjusted according to the shallow shell equations. The convergence properties of the method are investigated by comparing experimental data of PMMA cylinders to theoretical and numerical predictions. The applicability of the technique to non-developable surfaces is verified. It is demonstrated that robust convergence requires the number of terms in the Williams expansion exceeding 6. For different geometries, the ratio of the data selection radius and the length of the crack should exceed 0.3

Experimental investigation on the fatigue and fracture properties of a fine pearlitic rail steel

This study reports an experimental investigation of the fatigue and fracture resistance of R350HT, a heat-treated pearlitic rail steel with refined microstructure used in rails. Monotonic tensile, rotating bending, linear elastic plane strain fracture toughness, and fatigue crack growth rate tests are presented. The results are used to outline the basic properties and are corroborated by fractographic investigation. This enables the identification of the dominant type of fracture. Regarding fatigue and fracture resistance, the investigated material shows similar properties as other pearlitic rail steels, such as R260. At room temperature, the dominating fracture is of brittle cleavage type, showing some ductile regions associated with pro-eutectoid.   &nbsp

On the stress- and strain-based fatigue behavior of welded thick-walled nodular cast iron

Nodular cast iron (GJS) represents one of the most widely used materials for vehicle, energy and heavy machinery industry. Nevertheless, foundries struggling with the influence of local material defects in GJS like pores, shrinkages and dross often leading to a locally reduced fatigue strength of the entire component. One measure to tackle those negative effects is the welding of the affected areas. This measure is then successful when locally achieved material strengths and surface qualities are higher than the component with the casting defect. Unfortunately, data for the lifetime and fatigue assessment of welded GJS are not present right now. Thus, the research project »nodularWELD« assessed the local stress- and strain-based fatigue data of different thick-walled GJS grades for building a basis for a successful usage even of defect affected components. So, three ferritic and pearlitic GJS grades were investigated in the heat-affected zone, the base material, the welding filler and more over in an integral material state comprising all those three aforementioned states based on axial and bending investigations. Additionally metallographic and fractographic analysis were conducted